ArticleScientific reports2021
Deep learning of material transport in complex neurite networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics.Computer methods in applied mechanics and engineering · 2023Article
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
- Modeling neuron growth using isogeometric collocation based phase field method.Scientific reports · 2022Article
- Dynamic mode decomposition in adaptive mesh refinement and coarsening simulations.Engineering with computers · 2022Article
- Brain aging mechanisms with mechanical manifestations.Mechanisms of ageing and development · 2021Review
- Radiological tumor classification across imaging modality and histology.Nature machine intelligence · 2021Article
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3 authors.
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Abstract
Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and [Formula: see text] times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.
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